Special Sessions

Continuous monitoring of physiological parameters such as heart and respiratory rate, blood pressure, blood-oxygen saturation, ECG (Electrocardiogram), EEG (Electroencephalogram) and many other parameters provides crucial information for the assessment of the healthy status of the patients. Such information may facilitate early diagnosis of diseases, timely intervention or prediction of potential risks. Moreover, ambulatory healthy monitoring with wearable devices has a promising role in the delivery of healthcare to patients with limited access to in-hospital services. 

This special session aims to unravel the intricate interplay between physiological signal processing and emotion recognition using wearable technology. With the ubiquitous integration of wearable devices into our daily lives, there is growing interest in harnessing these technologies to monitor and interpret physiological signals, providing profound insights into individuals' emotional states. The session will encompass diverse disciplines, including biomedical engineering, signal processing, and human-computer interaction, fostering discussions on state-of-the-art theories, methodologies, and applications. 

Prioritizing health is critical for prosperity and economic growth. Skin patches and ultra-thing wearables allow for remote and (quasi)-continuous quantification of health. This enables actionable feedback for prevention, early diagnosis, and disease management.  

The advent of novel wearable sensor technologies, spanning smartwatches, chest patches, glass-based frames, and quasi/non-contact sensors, presents an array of challenges ripe for exploration. Addressing these challenges entails the deployment of innovative and robust signal processing techniques, such as denoising, artifact rejection, and waveform reshaping, powered by the prowess of machine and deep learning algorithms. However, beyond the technical frontiers lies the immense value embedded in the integration of these sensors with established biosignals like electrocardiogram, seismocardiogram, photoplethysmogram, and phonocardiogram, and associated with accelerometric patterns for actigraphy analysis. 

Measuring human kinematics and kinetics outside the gait laboratory has become more accessible over the last decade, with the miniaturization (and cost reduction) of IMUs and pressure insoles. This provides great opportunities for measuring human biomechanics in environments that are relevant to the users of interest (e.g., patients in home/clinical settings and athletes in their sports specific environment). 

The ability to take Medical Measurements using new methods with new sensors of a growing technical diversity favors the development of Medical Technology. When human movement is involved, measurements include time and space in addition to other magnitudes such as force, torque and weight. Rehabilitation and sports medicine study gait as a key feature of human well being, performance and therapeutic procedures. Biomechanics, as part of Biomedical Engineering, helps clinicians in the major task of quantification of 3D movement. 

Technological advances have greatly facilitated the widespread use of body-worn sensors to monitor various health conditions, generating large amounts of physiological data. Those signals, in combination with machine learning technologies, can be used to drive diagnostic innovations that enable early diagnosis of disease and assessment of health status to promote human well-being.  
The inevitable noise caused by the interference coming from other organs makes the interpretation of the signals a real challenge. To overcome this difficulty, mathematical transformation techniques are often used to extract features that represent the signals in a simple, low-dimensional way and are used to train machine learning algorithms to develop diagnostic tools. Clinical application of such a system requires biological or physiological interpretation of these features. Computational biology can provide information and insight into the origins of measured physiological signals and suggest new ways to interpret them. For example, modeling of the heart has led to a thorough understanding of cardiac activity, which has proven to be an invaluable aid in the diagnosis of many heart diseases.  

One in every five pregnancies leads to complications. For almost one in every hundred pregnancies, the baby does not survive. Pregnancy monitoring is of vital importance to decrease perinatal mortality, but is strongly hampered by the lack of high-quality and reliable methods to measure the vital signs of the unborn child (i.e. fetus). The maternal womb provides a safe environment for the fetus to develop and grow, but also makes it difficult to gather information on the fetal well-being.

This session will be open to papers that explore advances in neonatal monitoring. Innovations in this field spans diverse domains, featuring non-invasive monitoring methodologies, cutting-edge sensors and imaging modalities, giving comprehensive insights into vital parameters without imposing undue stress on fragile neonates. This includes also home monitoring applications. 

The use of 3D printed models for planning and simulation of surgical activity, as well as of biological substitutes for transplant, is becoming a more and more widespread and effective tool for a new personalized medicine approach. Indeed, novel 3D printing techniques, as Stereo Lithography, allow for the realization of complex geometries, also in combination with imaging techniques, as for example, Computer Tomography and Magnetic Resonance Imaging. In this context, the development of novel measurement systems and testing protocols plays a crucial role for a two-fold reason: from the one end, the mechanical properties of newly printed materials must be as close as possible to the target ones, both for transplant and surgical activities; from the other end, the results of the mechanical testing would provide effective feedback to improve the printing processes and materials itself.  

Additive manufacturing, or 3D printing, is revolutionizing the field of healthcare by enabling the bespoke production of smart materials and devices. This special session aims to explore the cutting-edge developments in the additive manufacturing and fabrication of smart materials for healthcare and medical applications. The focus will be on innovative techniques and materials that are shaping the future of wearable sensors, actuators, and smart medical devices. 

The integration of artificial intelligence (AI) into healthcare has recently witnessed a remarkable acceleration, with a primary focus on expediting and enhancing diagnosis accuracy. This special session explores the growing interest among healthcare specialists and researchers in adopting and developing cutting-edge algorithms, including deep learning, brain connectivity networking, advanced signal processing, and more. 

Medical devices in neurosciences are revolutionizing the medical field. Devices including brain monitoring systems, neuroprosthetics, and neurostimulators are both of high clinical interest and rapid progress. Among them, technology for neural sensing is obviously directly required for all monitoring and imaging devices, such as electroencephalographs, neuroimaging (MRI, PET, MEG), electrical impedance tomography, neurochemical monitoring, wearables to detect specific neurological conditions (e.g., the occurrence of a seizure), etc.

This Special Session on Neuroengineering intends to encourage submission of original research papers concerning models, experimental signal processing and imaging-based techniques, technological innovations, and devices that can interface with the nervous system. The field of neuroengineering comprises fundamental, experimental, computational, theoretical, and quantitative research aimed at understanding and augmenting brain function in health and disease across multiple spatiotemporal scales. To this purpose, the field draws on computational neuroscience, experimental neuroscience, neurology, electrical engineering, and signal processing. 

In recent years, the landscape of critical care has undergone a transformative shift, propelled by technological advancements that have revolutionized the way healthcare professionals monitor and manage patients in intensive care settings. The integration of cutting-edge monitoring technologies has not only enhanced the precision of vital sign measurements but has also provided invaluable insights into patient physiology, enabling a more proactive and personalized approach to critical care. As we stand at the intersection of healthcare and technology, the need for a collaborative platform to discuss, evaluate, and propel these innovations forward becomes increasingly evident. 

Although we spend about one third of our lives sleeping, the exact function of sleep is still poorly understood. We do know however, that sufficient sleep is essential for general health and quality of life. People with sleep disorders do not only face short term consequences such as fatigue, they are at higher risk of developing other disorders such as obesity, cardiovascular disease and dementia.  

This session aims to highlight the pivotal role of Magnetic Resonance Imaging (MRI) techniques specifically in image-guided therapies and interventions, emphasizing the unique procedural feedback that can be derived from MRI data. The session will review the different MRI methods used to characterize tissue, navigate the anatomical roadmap and assess treatment response within various image-guided diagnostic or therapeutic procedures. The primary objective is to deepen our understanding of how this information can improve the precision, efficacy and safety of image-guided therapeutic interventions in various medical disciplines. In addition, this session aims to foster interdisciplinary dialogue, disseminate cutting-edge research, and pave the way for the use of MRI as indispensable tool in the optimization and refinement of image-guided therapies and interventions, ultimately leading to improved patient outcomes. 

Ultrasound is a widely used imaging modality that is known for its high spatial and temporal resolution, and its cost-effective and non-invasive nature. Recent advances in imaging hardware, image reconstruction and analysis, and the role of artificial intelligence have broadened the use of ultrasound even further, and have introduced medical sonography in the GP's office. In this special session, we will see exciting new developments in the field of medical ultrasound, and their journey from bench to bedside. 

Echography is one of the most important medical diagnostic techniques available. However, it requires a skilled sonographer to aim the transducer and to interpret the images. This, combined with the formfactor and rigidity of the transducer and the bulkiness of the equipment, causes conventional non-invasive echography approaches to be poorly suited for continuous measurements or monitoring – particularly in settings outside the clinics without expert sonographers.  

Interpretability and explainability have always been essential in medical imaging analysis. To establish trustworthy processing workflows and clinical decision support systems, it is crucial to design transparent and user-oriented approaches. These requirements are even more critical today with the increasing prevalence of machine and deep learning tools. Consequently, both researchers and funding initiatives, such as those embraced by the European Union, are dedicated to proposing and encouraging actions to overcome the challenges posed by black box methods in medical image processing and analysis. This special issue aims to present and discuss innovative approaches for the development of interpretable and explainable methods and tools for image analysis and processing in the medical field. 

Arterial disease is responsible for a significant amount of suffering and socioeconomic costs in the world. Though in the past 50 years the diagnosis and treatment have developed to high standards, two key areas present real challenges.  

Probably, there is not a medical specialty that relies on the acquisition, recording, and analysis of signals and images more than cardiology. A lot of technological and methodological advances have been introduced since the days when stethoscopes and blood pressure cuffs were the only diagnostic equipment available to assess the cardiovascular system of a patient. Nowadays it’s impossible to imagine modern cardiology without electrocardiograms, continuous blood pressure monitoring, intracardiac electrograms, echocardiograms, CTs and MRIs. 

Non-healing wounds are known as a significant, debilitating condition that is growing within hospitals and home-care. These wounds can remain from month’s to years, causing extreme pain for patients, specifically seen within diabetic, obese, elderly and immunocompromised populations. With the notified reduction in experienced healthcare professionals foreseen and the framework for clinical assessment and treatment decision making based on touch, sight and smell, the need for digital tools to be incorporated within the healthcare framework is essential. 

In the last decades, electrochemical and mechanical sensors have gained increasing importance in the biomedical field thanks to their non-invasiveness and widespread applications. They have been employed to monitor the concentration of metabolites, electrolytes, drugs, hormones, physiological parameters, vital signs, and disease markers. Thanks to the possibility of developing small-size solutions and the possibility of acquiring their signal using simple and low-cost electronics, many electrochemical and mechanical sensors are now part of wearable devices and point-of-cares, more and more affordable and spreadable.

Flexible electronics provides a whole series of design opportunities that will lead to the creation of a range of future electronic applications across a number of sectors including within healthcare. Wearable healthcare technologies and point-of-care diagnostics are particular applications for flexible electronics that can allow effective health monitoring, while minimizing stress and discomfort to the patients, and therefore enhancing their quality of life, as well as reducing healthcare costs. 

The intersection of Artificial Intelligence (AI), Computer Vision, Robotics, and Virtual reality/augmented reality (VR/AR) technologies presents a transformative approach to rehabilitation medicine. This proposal outlines a comprehensive framework that combines these cutting-edge technologies for the analysis, assistance, and augmentation of rehabilitation processes. The aim is to enhance motor function assessment, support recovery through robotic assistance, and provide immersive therapy experiences for patients with various motor and cognitive impairments.